Triple

T20137670
Position Surface form Disambiguated ID Type / Status
Subject Maardu E491064 entity
Predicate hasIndustrialArea P40 FINISHED
Object Maardu industrial district NE NERFINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Maardu industrial district | Statement: [Maardu, hasIndustrialArea, Maardu industrial district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maardu industrial district
Context triple: [Maardu, hasIndustrialArea, Maardu industrial district]
  • A. Maardu chosen
    Maardu is an industrial town in northern Estonia, located just east of the capital Tallinn in Harju County.
  • B. Muroran industrial zone
    Muroran industrial zone is a major coastal industrial area in Muroran, Hokkaido, known for its heavy industries such as steel, shipbuilding, and petrochemicals.
  • C. Altona industrial precinct
    Altona industrial precinct is a major industrial and manufacturing hub in Melbourne’s western suburbs, hosting a range of heavy industry, logistics, and warehousing facilities.
  • D. Kraainem
    Kraainem is a Dutch- and French-speaking suburban municipality on the eastern edge of Brussels in the Flemish Brabant province of Belgium.
  • E. Švermov industrial area
    Švermov industrial area is a major industrial zone in the city of Kladno in the Czech Republic, historically associated with heavy industry and manufacturing.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69da62651a0c8190a3e05e95e056a66b completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e6676879f48190a59da04393d2a8cc completed April 20, 2026, 5:50 p.m.
Created at: April 11, 2026, 11:32 p.m.